typing — Support for type hints

New in version 3.5.

Source code: Lib/typing.py

Note

The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.

This module provides runtime support for type hints. The most fundamental support consists of the types Any, Union, Callable, TypeVar, and Generic. For a full specification, please see PEP 484. For a simplified introduction to type hints, see PEP 483.

The function below takes and returns a string and is annotated as follows:

def greeting(name: str) -> str:
    return 'Hello ' + name

In the function greeting, the argument name is expected to be of type str and the return type str. Subtypes are accepted as arguments.

New features are frequently added to the typing module. The typing_extensions package provides backports of these new features to older versions of Python.

See also

The documentation at https://typing.readthedocs.io/ serves as useful reference for type system features, useful typing related tools and typing best practices.

Relevant PEPs

Since the initial introduction of type hints in PEP 484 and PEP 483, a number of PEPs have modified and enhanced Python’s framework for type annotations. These include:

Type aliases

A type alias is defined by assigning the type to the alias. In this example, Vector and list[float] will be treated as interchangeable synonyms:

Vector = list[float]

def scale(scalar: float, vector: Vector) -> Vector:
    return [scalar * num for num in vector]

# typechecks; a list of floats qualifies as a Vector.
new_vector = scale(2.0, [1.0, -4.2, 5.4])

Type aliases are useful for simplifying complex type signatures. For example:

from collections.abc import Sequence

ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]

def broadcast_message(message: str, servers: Sequence[Server]) -> None:
    ...

# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
        message: str,
        servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
    ...

Note that None as a type hint is a special case and is replaced by type(None).

NewType

Use the NewType helper to create distinct types:

from typing import NewType

UserId = NewType('UserId', int)
some_id = UserId(524313)

The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors:

def get_user_name(user_id: UserId) -> str:
    ...

# typechecks
user_a = get_user_name(UserId(42351))

# does not typecheck; an int is not a UserId
user_b = get_user_name(-1)

You may still perform all int operations on a variable of type UserId, but the result will always be of type int. This lets you pass in a UserId wherever an int might be expected, but will prevent you from accidentally creating a UserId in an invalid way:

# 'output' is of type 'int', not 'UserId'
output = UserId(23413) + UserId(54341)

Note that these checks are enforced only by the static type checker. At runtime, the statement Derived = NewType('Derived', Base) will make Derived a callable that immediately returns whatever parameter you pass it. That means the expression Derived(some_value) does not create a new class or introduce much overhead beyond that of a regular function call.

More precisely, the expression some_value is Derived(some_value) is always true at runtime.

It is invalid to create a subtype of Derived:

from typing import NewType

UserId = NewType('UserId', int)

# Fails at runtime and does not typecheck
class AdminUserId(UserId): pass

However, it is possible to create a NewType based on a ‘derived’ NewType:

from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)

and typechecking for ProUserId will work as expected.

See PEP 484 for more details.

Note

Recall that the use of a type alias declares two types to be equivalent to one another. Doing Alias = Original will make the static type checker treat Alias as being exactly equivalent to Original in all cases. This is useful when you want to simplify complex type signatures.

In contrast, NewType declares one type to be a subtype of another. Doing Derived = NewType('Derived', Original) will make the static type checker treat Derived as a subclass of Original, which means a value of type Original cannot be used in places where a value of type Derived is expected. This is useful when you want to prevent logic errors with minimal runtime cost.

New in version 3.5.2.

Changed in version 3.10: NewType is now a class rather than a function. There is some additional runtime cost when calling NewType over a regular function. However, this cost will be reduced in 3.11.0.

Callable

Frameworks expecting callback functions of specific signatures might be type hinted using Callable[[Arg1Type, Arg2Type], ReturnType].

For example:

from collections.abc import Callable

def feeder(get_next_item: Callable[[], str]) -> None:
    # Body

def async_query(on_success: Callable[[int], None],
                on_error: Callable[[int, Exception], None]) -> None:
    # Body

async def on_update(value: str) -> None:
    # Body
callback: Callable[[str], Awaitable[None]] = on_update

It is possible to declare the return type of a callable without specifying the call signature by substituting a literal ellipsis for the list of arguments in the type hint: Callable[..., ReturnType].

Callables which take other callables as arguments may indicate that their parameter types are dependent on each other using ParamSpec. Additionally, if that callable adds or removes arguments from other callables, the Concatenate operator may be used. They take the form Callable[ParamSpecVariable, ReturnType] and Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] respectively.

Changed in version 3.10: Callable now supports ParamSpec and Concatenate. See PEP 612 for more information.

See also

The documentation for ParamSpec and Concatenate provides examples of usage in Callable.

Generics

Since type information about objects kept in containers cannot be statically inferred in a generic way, abstract base classes have been extended to support subscription to denote expected types for container elements.

from collections.abc import Mapping, Sequence

def notify_by_email(employees: Sequence[Employee],
                    overrides: Mapping[str, str]) -> None: ...

Generics can be parameterized by using a factory available in typing called TypeVar.

from collections.abc import Sequence
from typing import TypeVar

T = TypeVar('T')      # Declare type variable

def first(l: Sequence[T]) -> T:   # Generic function
    return l[0]

User-defined generic types

A user-defined class can be defined as a generic class.

from typing import TypeVar, Generic
from logging import Logger

T = TypeVar('T')

class LoggedVar(Generic[T]):
    def __init__(self, value: T, name: str, logger: Logger) -> None:
        self.name = name
        self.logger = logger
        self.value = value

    def set(self, new: T) -> None:
        self.log('Set ' + repr(self.value))
        self.value = new

    def get(self) -> T:
        self.log('Get ' + repr(self.value))
        return self.value

    def log(self, message: str) -> None:
        self.logger.info('%s: %s', self.name, message)

Generic[T] as a base class defines that the class LoggedVar takes a single type parameter T . This also makes T valid as a type within the class body.

The Generic base class defines __class_getitem__() so that LoggedVar[t] is valid as a type:

from collections.abc import Iterable

def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
    for var in vars:
        var.set(0)

A generic type can have any number of type variables. All varieties of TypeVar are permissible as parameters for a generic type:

from typing import TypeVar, Generic, Sequence

T = TypeVar('T', contravariant=True)
B = TypeVar('B', bound=Sequence[bytes], covariant=True)
S = TypeVar('S', int, str)

class WeirdTrio(Generic[T, B, S]):
    ...

Each type variable argument to Generic must be distinct. This is thus invalid:

from typing import TypeVar, Generic
...

T = TypeVar('T')

class Pair(Generic[T, T]):   # INVALID
    ...

You can use multiple inheritance with Generic:

from collections.abc import Sized
from typing import TypeVar, Generic

T = TypeVar('T')

class LinkedList(Sized, Generic[T]):
    ...

When inheriting from generic classes, some type variables could be fixed:

from collections.abc import Mapping
from typing import TypeVar

T = TypeVar('T')

class MyDict(Mapping[str, T]):
    ...

In this case MyDict has a single parameter, T.

Using a generic class without specifying type parameters assumes Any for each position. In the following example, MyIterable is not generic but implicitly inherits from Iterable[Any]:

from collections.abc import Iterable

class MyIterable(Iterable): # Same as Iterable[Any]

User defined generic type aliases are also supported. Examples:

from collections.abc import Iterable
from typing import TypeVar
S = TypeVar('S')
Response = Iterable[S] | int

# Return type here is same as Iterable[str] | int
def response(query: str) -> Response[str]:
    ...

T = TypeVar('T', int, float, complex)
Vec = Iterable[tuple[T, T]]

def inproduct(v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
    return sum(x*y for x, y in v)

Changed in version 3.7: Generic no longer has a custom metaclass.

User-defined generics for parameter expressions are also supported via parameter specification variables in the form Generic[P]. The behavior is consistent with type variables’ described above as parameter specification variables are treated by the typing module as a specialized type variable. The one exception to this is that a list of types can be used to substitute a ParamSpec:

>>> from typing import Generic, ParamSpec, TypeVar

>>> T = TypeVar('T')
>>> P = ParamSpec('P')

>>> class Z(Generic[T, P]): ...
...
>>> Z[int, [dict, float]]
__main__.Z[int, (<class 'dict'>, <class 'float'>)]

Furthermore, a generic with only one parameter specification variable will accept parameter lists in the forms X[[Type1, Type2, ...]] and also X[Type1, Type2, ...] for aesthetic reasons. Internally, the latter is converted to the former, so the following are equivalent:

>>> class X(Generic[P]): ...
...
>>> X[int, str]
__main__.X[(<class 'int'>, <class 'str'>)]
>>> X[[int, str]]
__main__.X[(<class 'int'>, <class 'str'>)]

Do note that generics with ParamSpec may not have correct __parameters__ after substitution in some cases because they are intended primarily for static type checking.

Changed in version 3.10: Generic can now be parameterized over parameter expressions. See ParamSpec and PEP 612 for more details.

A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality.

The Any type

A special kind of type is Any. A static type checker will treat every type as being compatible with Any and Any as being compatible with every type.

This means that it is possible to perform any operation or method call on a value of type Any and assign it to any variable:

from typing import Any

a: Any = None
a = []          # OK
a = 2           # OK

s: str = ''
s = a           # OK

def foo(item: Any) -> int:
    # Typechecks; 'item' could be any type,
    # and that type might have a 'bar' method
    item.bar()
    ...

Notice that no typechecking is performed when assigning a value of type Any to a more precise type. For example, the static type checker did not report an error when assigning a to s even though s was declared to be of type str and receives an int value at runtime!

Furthermore, all functions without a return type or parameter types will implicitly default to using Any:

def legacy_parser(text):
    ...
    return data

# A static type checker will treat the above
# as having the same signature as:
def legacy_parser(text: Any) -> Any:
    ...
    return data

This behavior allows Any to be used as an escape hatch when you need to mix dynamically and statically typed code.

Contrast the behavior of Any with the behavior of object. Similar to Any, every type is a subtype of object. However, unlike Any, the reverse is not true: object is not a subtype of every other type.

That means when the type of a value is object, a type checker will reject almost all operations on it, and assigning it to a variable (or using it as a return value) of a more specialized type is a type error. For example:

def hash_a(item: object) -> int:
    # Fails; an object does not have a 'magic' method.
    item.magic()
    ...

def hash_b(item: Any) -> int:
    # Typechecks
    item.magic()
    ...

# Typechecks, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")

# Typechecks, since Any is compatible with all types
hash_b(42)
hash_b("foo")

Use object to indicate that a value could be any type in a typesafe manner. Use Any to indicate that a value is dynamically typed.

Nominal vs structural subtyping

Initially PEP 484 defined the Python static type system as using nominal subtyping. This means that a class A is allowed where a class B is expected if and only if A is a subclass of B.

This requirement previously also applied to abstract base classes, such as Iterable. The problem with this approach is that a class had to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to PEP 484:

from collections.abc import Sized, Iterable, Iterator

class Bucket(Sized, Iterable[int]):
    ...
    def __len__(self) -> int: ...
    def __iter__(self) -> Iterator[int]: ...

PEP 544 allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing Bucket to be implicitly considered a subtype of both Sized and Iterable[int] by static type checkers. This is known as structural subtyping (or static duck-typing):

from collections.abc import Iterator, Iterable

class Bucket:  # Note: no base classes
    ...
    def __len__(self) -> int: ...
    def __iter__(self) -> Iterator[int]: ...

def collect(items: Iterable[int]) -> int: ...
result = collect(Bucket())  # Passes type check

Moreover, by subclassing a special class Protocol, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).

Module contents

The module defines the following classes, functions and decorators.

Note

This module defines several types that are subclasses of pre-existing standard library classes which also extend Generic to support type variables inside []. These types became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support [].

The redundant types are deprecated as of Python 3.9 but no deprecation warnings will be issued by the interpreter. It is expected that type checkers will flag the deprecated types when the checked program targets Python 3.9 or newer.

The deprecated types will be removed from the typing module in the first Python version released 5 years after the release of Python 3.9.0. See details in PEP 585—Type Hinting Generics In Standard Collections.

Special typing primitives

Special types

These can be used as types in annotations and do not support [].

typing.Any

Special type indicating an unconstrained type.

  • Every type is compatible with Any.

  • Any is compatible with every type.

typing.NoReturn

Special type indicating that a function never returns. For example:

from typing import NoReturn

def stop() -> NoReturn:
    raise RuntimeError('no way')

New in version 3.5.4.

New in version 3.6.2.

typing.TypeAlias

Special annotation for explicitly declaring a type alias. For example:

from typing import TypeAlias

Factors: TypeAlias = list[int]

See PEP 613 for more details about explicit type aliases.

New in version 3.10.

Special forms

These can be used as types in annotations using [], each having a unique syntax.

typing.Tuple

Tuple type; Tuple[X, Y] is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written as Tuple[()].

Example: Tuple[T1, T2] is a tuple of two elements corresponding to type variables T1 and T2. Tuple[int, float, str] is a tuple of an int, a float and a string.

To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g. Tuple[int, ...]. A plain Tuple is equivalent to Tuple[Any, ...], and in turn to tuple.

Deprecated since version 3.9: builtins.tuple now supports []. See PEP 585 and Generic Alias Type.

typing.Union

Union type; Union[X, Y] is equivalent to X | Y and means either X or Y.

To define a union, use e.g. Union[int, str] or the shorthand int | str. Using that shorthand is recommended. Details:

  • The arguments must be types and there must be at least one.

  • Unions of unions are flattened, e.g.:

    Union[Union[int, str], float] == Union[int, str, float]

  • Unions of a single argument vanish, e.g.:

    Union[int] == int  # The constructor actually returns int

  • Redundant arguments are skipped, e.g.:

    Union[int, str, int] == Union[int, str] == int | str

  • When comparing unions, the argument order is ignored, e.g.:

    Union[int, str] == Union[str, int]

  • You cannot subclass or instantiate a Union.

  • You cannot write Union[X][Y].

Changed in version 3.7: Don’t remove explicit subclasses from unions at runtime.

Changed in version 3.10: Unions can now be written as X | Y. See union type expressions.

typing.Optional

Optional type.

Optional[X] is equivalent to X | None (or Union[X, None]).

Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the Optional qualifier on its type annotation just because it is optional. For example:

def foo(arg: int = 0) -> None:
    ...

On the other hand, if an explicit value of None is allowed, the use of Optional is appropriate, whether the argument is optional or not. For example:

def foo(arg: Optional[int] = None) -> None:
    ...

Changed in version 3.10: Optional can now be written as X | None. See union type expressions.

typing.Callable

Callable type; Callable[[int], str] is a function of (int) -> str.

The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types or an ellipsis; the return type must be a single type.

There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types. Callable[..., ReturnType] (literal ellipsis) can be used to type hint a callable taking any number of arguments and returning ReturnType. A plain Callable is equivalent to Callable[..., Any], and in turn to collections.abc.Callable.

Callables which take other callables as arguments may indicate that their parameter types are dependent on each other using ParamSpec. Additionally, if that callable adds or removes arguments from other callables, the Concatenate operator may be used. They take the form Callable[ParamSpecVariable, ReturnType] and Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] respectively.

Deprecated since version 3.9: collections.abc.Callable now supports []. See PEP 585 and Generic Alias Type.

Changed in version 3.10: Callable now supports ParamSpec and Concatenate. See PEP 612 for more information.

See also

The documentation for ParamSpec and Concatenate provide examples of usage with Callable.

typing.Concatenate

Used with Callable and ParamSpec to type annotate a higher order callable which adds, removes, or transforms parameters of another callable. Usage is in the form Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]. Concatenate is currently only valid when used as the first argument to a Callable. The last parameter to Concatenate must be a ParamSpec.

For example, to annotate a decorator with_lock which provides a threading.Lock to the decorated function, Concatenate can be used to indicate that with_lock expects a callable which takes in a Lock as the first argument, and returns a callable with a different type signature. In this case, the ParamSpec indicates that the returned callable’s parameter types are dependent on the parameter types of the callable being passed in:

from collections.abc import Callable
from threading import Lock
from typing import Concatenate, ParamSpec, TypeVar

P = ParamSpec('P')
R = TypeVar('R')

# Use this lock to ensure that only one thread is executing a function
# at any time.
my_lock = Lock()

def with_lock(f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
    '''A type-safe decorator which provides a lock.'''
    def inner(*args: P.args, **kwargs: P.kwargs) -> R:
        # Provide the lock as the first argument.
        return f(my_lock, *args, **kwargs)
    return inner

@with_lock
def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
    '''Add a list of numbers together in a thread-safe manner.'''
    with lock:
        return sum(numbers)

# We don't need to pass in the lock ourselves thanks to the decorator.
sum_threadsafe([1.1, 2.2, 3.3])

New in version 3.10.

See also

  • PEP 612 – Parameter Specification Variables (the PEP which introduced ParamSpec and Concatenate).

  • ParamSpec and Callable.

class typing.Type(Generic[CT_co])

A variable annotated with C may accept a value of type C. In contrast, a variable annotated with Type[C] may accept values that are classes themselves – specifically, it will accept the class object of C. For example:

a = 3         # Has type 'int'
b = int       # Has type 'Type[int]'
c = type(a)   # Also has type 'Type[int]'

Note that Type[C] is covariant:

class User: ...
class BasicUser(User): ...
class ProUser(User): ...
class TeamUser(User): ...

# Accepts User, BasicUser, ProUser, TeamUser, ...
def make_new_user(user_class: Type[User]) -> User:
    # ...
    return user_class()

The fact that Type[C] is covariant implies that all subclasses of C should implement the same constructor signature and class method signatures as C. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484.

The only legal parameters for Type are classes, Any, type variables, and unions of any of these types. For example:

def new_non_team_user(user_class: Type[BasicUser | ProUser]): ...

Type[Any] is equivalent to Type which in turn is equivalent to type, which is the root of Python’s metaclass hierarchy.

New in version 3.5.2.

Deprecated since version 3.9: builtins.type now supports []. See PEP 585 and Generic Alias Type.

typing.Literal

A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example:

def validate_simple(data: Any) -> Literal[True]:  # always returns True
    ...

MODE = Literal['r', 'rb', 'w', 'wb']
def open_helper(file: str, mode: MODE) -> str:
    ...

open_helper('/some/path', 'r')  # Passes type check
open_helper('/other/path', 'typo')  # Error in type checker

Literal[...] cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to Literal[...], but type checkers may impose restrictions. See PEP 586 for more details about literal types.

New in version 3.8.

Changed in version 3.9.1: Literal now de-duplicates parameters. Equality comparisons of Literal objects are no longer order dependent. Literal objects will now raise a TypeError exception during equality comparisons if one of their parameters are not hashable.

typing.ClassVar

Special type construct to mark class variables.

As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:

class Starship:
    stats: ClassVar[dict[str, int]] = {} # class variable
    damage: int = 10                     # instance variable

ClassVar accepts only types and cannot be further subscribed.

ClassVar is not a class itself, and should not be used with isinstance() or issubclass(). ClassVar does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:

enterprise_d = Starship(3000)
enterprise_d.stats = {} # Error, setting class variable on instance
Starship.stats = {}     # This is OK

New in version 3.5.3.

typing.Final

A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example:

MAX_SIZE: Final = 9000
MAX_SIZE += 1  # Error reported by type checker

class Connection:
    TIMEOUT: Final[int] = 10

class FastConnector(Connection):
    TIMEOUT = 1  # Error reported by type checker

There is no runtime checking of these properties. See PEP 591 for more details.

New in version 3.8.

typing.Annotated

A type, introduced in PEP 593 (Flexible function and variable annotations), to decorate existing types with context-specific metadata (possibly multiple pieces of it, as Annotated is variadic). Specifically, a type T can be annotated with metadata x via the typehint Annotated[T, x]. This metadata can be used for either static analysis or at runtime. If a library (or tool) encounters a typehint Annotated[T, x] and has no special logic for metadata x, it should ignore it and simply treat the type as T. Unlike the no_type_check functionality that currently exists in the typing module which completely disables typechecking annotations on a function or a class, the Annotated type allows for both static typechecking of T (which can safely ignore x) together with runtime access to x within a specific application.

Ultimately, the responsibility of how to interpret the annotations (if at all) is the responsibility of the tool or library encountering the Annotated type. A tool or library encountering an Annotated type can scan through the annotations to determine if they are of interest (e.g., using isinstance()).

When a tool or a library does not support annotations or encounters an unknown annotation it should just ignore it and treat annotated type as the underlying type.

It’s up to the tool consuming the annotations to decide whether the client is allowed to have several annotations on one type and how to merge those annotations.

Since the Annotated type allows you to put several annotations of the same (or different) type(s) on any node, the tools or libraries consuming those annotations are in charge of dealing with potential duplicates. For example, if you are doing value range analysis you might allow this:

T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]

Passing include_extras=True to get_type_hints() lets one access the extra annotations at runtime.

The details of the syntax:

  • The first argument to Annotated must be a valid type

  • Multiple type annotations are supported (Annotated supports variadic arguments):

    Annotated[int, ValueRange(3, 10), ctype("char")]

  • Annotated must be called with at least two arguments ( Annotated[int] is not valid)

  • The order of the annotations is preserved and matters for equality checks:

    Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
    int, ctype("char"), ValueRange(3, 10)
]

  • Nested Annotated types are flattened, with metadata ordered starting with the innermost annotation:

    Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
    int, ValueRange(3, 10), ctype("char")
]

  • Duplicated annotations are not removed:

    Annotated[int, ValueRange(3, 10)] != Annotated[
    int, ValueRange(3, 10), ValueRange(3, 10)
]

  • Annotated can be used with nested and generic aliases:

    T = TypeVar('T')
Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
V = Vec[int]

V == Annotated[list[tuple[int, int]], MaxLen(10)]

New in version 3.9.

typing.TypeGuard

Special typing form used to annotate the return type of a user-defined type guard function. TypeGuard only accepts a single type argument. At runtime, functions marked this way should return a boolean.

TypeGuard aims to benefit type narrowing – a technique used by static type checkers to determine a more precise type of an expression within a program’s code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a “type guard”:

def is_str(val: str | float):
    # "isinstance" type guard
    if isinstance(val, str):
        # Type of ``val`` is narrowed to ``str``
        ...
    else:
        # Else, type of ``val`` is narrowed to ``float``.
        ...

Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use TypeGuard[...] as its return type to alert static type checkers to this intention.

Using -> TypeGuard tells the static type checker that for a given function:

  1. The return value is a boolean.

  2. If the return value is True, the type of its argument is the type inside TypeGuard.

For example:

def is_str_list(val: List[object]) -> TypeGuard[List[str]]:
    '''Determines whether all objects in the list are strings'''
    return all(isinstance(x, str) for x in val)

def func1(val: List[object]):
    if is_str_list(val):
        # Type of ``val`` is narrowed to ``List[str]``.
        print(" ".join(val))
    else:
        # Type of ``val`` remains as ``List[object]``.
        print("Not a list of strings!")

If is_str_list is a class or instance method, then the type in TypeGuard maps to the type of the second parameter after cls or self.

In short, the form def foo(arg: TypeA) -> TypeGuard[TypeB]: ..., means that if foo(arg) returns True, then arg narrows from TypeA to TypeB.

Note

TypeB need not be a narrower form of TypeA – it can even be a wider form. The main reason is to allow for things like narrowing List[object] to List[str] even though the latter is not a subtype of the former, since List is invariant. The responsibility of writing type-safe type guards is left to the user.

TypeGuard also works with type variables. For more information, see PEP 647 (User-Defined Type Guards).

New in version 3.10.

Building generic types

These are not used in annotations. They are building blocks for creating generic types.

class typing.Generic

Abstract base class for generic types.

A generic type is typically declared by inheriting from an instantiation of this class with one or more type variables. For example, a generic mapping type might be defined as:

class Mapping(Generic[KT, VT]):
    def __getitem__(self, key: KT) -> VT:
        ...
        # Etc.

This class can then be used as follows:

X = TypeVar('X')
Y = TypeVar('Y')

def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
    try:
        return mapping[key]
    except KeyError:
        return default
class typing.TypeVar

Type variable.

Usage:

T = TypeVar('T')  # Can be anything
S = TypeVar('S', bound=str)  # Can be any subtype of str
A = TypeVar('A', str, bytes)  # Must be exactly str or bytes

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See Generic for more information on generic types. Generic functions work as follows:

def repeat(x: T, n: int) -> Sequence[T]:
    """Return a list containing n references to x."""
    return [x]*n


def print_capitalized(x: S) -> S:
    """Print x capitalized, and return x."""
    print(x.capitalize())
    return x


def concatenate(x: A, y: A) -> A:
    """Add two strings or bytes objects together."""
    return x + y

Note that type variables can be bound, constrained, or neither, but cannot be both bound and constrained.

Constrained type variables and bound type variables have different semantics in several important ways. Using a constrained type variable means that the TypeVar can only ever be solved as being exactly one of the constraints given:

a = concatenate('one', 'two')  # Ok, variable 'a' has type 'str'
b = concatenate(StringSubclass('one'), StringSubclass('two'))  # Inferred type of variable 'b' is 'str',
                                                               # despite 'StringSubclass' being passed in
c = concatenate('one', b'two')  # error: type variable 'A' can be either 'str' or 'bytes' in a function call, but not both

Using a bound type variable, however, means that the TypeVar will be solved using the most specific type possible:

print_capitalized('a string')  # Ok, output has type 'str'

class StringSubclass(str):
    pass

print_capitalized(StringSubclass('another string'))  # Ok, output has type 'StringSubclass'
print_capitalized(45)  # error: int is not a subtype of str

Type variables can be bound to concrete types, abstract types (ABCs or protocols), and even unions of types:

U = TypeVar('U', bound=str|bytes)  # Can be any subtype of the union str|bytes
V = TypeVar('V', bound=SupportsAbs)  # Can be anything with an __abs__ method

Bound type variables are particularly useful for annotating classmethods that serve as alternative constructors. In the following example (by Raymond Hettinger), the type variable C is bound to the Circle class through the use of a forward reference. Using this type variable to annotate the with_circumference classmethod, rather than hardcoding the return type as Circle, means that a type checker can correctly infer the return type even if the method is called on a subclass:

import math

C = TypeVar('C', bound='Circle')

class Circle:
    """An abstract circle"""

    def __init__(self, radius: float) -> None:
        self.radius = radius

    # Use a type variable to show that the return type
    # will always be an instance of whatever ``cls`` is
    @classmethod
    def with_circumference(cls: type[C], circumference: float) -> C:
        """Create a circle with the specified circumference"""
        radius = circumference / (math.pi * 2)
        return cls(radius)


class Tire(Circle):
    """A specialised circle (made out of rubber)"""

    MATERIAL = 'rubber'


c = Circle.with_circumference(3)  # Ok, variable 'c' has type 'Circle'
t = Tire.with_circumference(4)  # Ok, variable 't' has type 'Tire' (not 'Circle')

At runtime, isinstance(x, T) will raise TypeError. In general, isinstance() and issubclass() should not be used with types.

Type variables may be marked covariant or contravariant by passing covariant=True or contravariant=True. See PEP 484 for more details. By default, type variables are invariant.

class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False)

Parameter specification variable. A specialized version of type variables.

Usage:

P = ParamSpec('P')

Parameter specification variables exist primarily for the benefit of static type checkers. They are used to forward the parameter types of one callable to another callable – a pattern commonly found in higher order functions and decorators. They are only valid when used in Concatenate, or as the first argument to Callable, or as parameters for user-defined Generics. See Generic for more information on generic types.

For example, to add basic logging to a function, one can create a decorator add_logging to log function calls. The parameter specification variable tells the type checker that the callable passed into the decorator and the new callable returned by it have inter-dependent type parameters:

from collections.abc import Callable
from typing import TypeVar, ParamSpec
import logging

T = TypeVar('T')
P = ParamSpec('P')

def add_logging(f: Callable[P, T]) -> Callable[P, T]:
    '''A type-safe decorator to add logging to a function.'''
    def inner(*args: P.args, **kwargs: P.kwargs) -> T:
        logging.info(f'{f.__name__} was called')
        return f(*args, **kwargs)
    return inner

@add_logging
def add_two(x: float, y: float) -> float:
    '''Add two numbers together.'''
    return x + y

Without ParamSpec, the simplest way to annotate this previously was to use a TypeVar with bound Callable[..., Any]. However this causes two problems:

  1. The type checker can’t type check the inner function because *args and **kwargs have to be typed Any.

  2. cast() may be required in the body of the add_logging decorator when returning the inner function, or the static type checker must be told to ignore the return inner.

args
kwargs

Since ParamSpec captures both positional and keyword parameters, P.args and P.kwargs can be used to split a ParamSpec into its components. P.args represents the tuple of positional parameters in a given call and should only be used to annotate *args. P.kwargs represents the mapping of keyword parameters to their values in a given call, and should be only be used to annotate **kwargs. Both attributes require the annotated parameter to be in scope. At runtime, P.args and P.kwargs are instances respectively of ParamSpecArgs and ParamSpecKwargs.

Parameter specification variables created with covariant=True or contravariant=True can be used to declare covariant or contravariant generic types. The bound argument is also accepted, similar to TypeVar. However the actual semantics of these keywords are yet to be decided.

New in version 3.10.

Note

Only parameter specification variables defined in global scope can be pickled.

See also

typing.ParamSpecArgs
typing.ParamSpecKwargs

Arguments and keyword arguments attributes of a ParamSpec. The P.args attribute of a ParamSpec is an instance of ParamSpecArgs, and P.kwargs is an instance of ParamSpecKwargs. They are intended for runtime introspection and have no special meaning to static type checkers.

Calling get_origin() on either of these objects will return the original ParamSpec:

P = ParamSpec("P")
get_origin(P.args)  # returns P
get_origin(P.kwargs)  # returns P

New in version 3.10.

typing.AnyStr

AnyStr is a constrained type variable defined as AnyStr = TypeVar('AnyStr', str, bytes).

It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:

def concat(a: AnyStr, b: AnyStr) -> AnyStr:
    return a + b

concat(u"foo", u"bar")  # Ok, output has type 'unicode'
concat(b"foo", b"bar")  # Ok, output has type 'bytes'
concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes
class typing.Protocol(Generic)

Base class for protocol classes. Protocol classes are defined like this:

class Proto(Protocol):
    def meth(self) -> int:
        ...

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check

See PEP 544 for details. Protocol classes decorated with runtime_checkable() (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.

Protocol classes can be generic, for example:

class GenProto(Protocol[T]):
    def meth(self) -> T:
        ...

New in version 3.8.

@typing.runtime_checkable

Mark a protocol class as a runtime protocol.

Such a protocol can be used with isinstance() and issubclass(). This raises TypeError when applied to a non-protocol class. This allows a simple-minded structural check, very similar to “one trick ponies” in collections.abc such as Iterable. For example:

@runtime_checkable
class Closable(Protocol):
    def close(self): ...

assert isinstance(open('/some/file'), Closable)

Note

runtime_checkable() will check only the presence of the required methods, not their type signatures. For example, ssl.SSLObject is a class, therefore it passes an issubclass() check against Callable. However, the ssl.SSLObject.__init__() method exists only to raise a TypeError with a more informative message, therefore making it impossible to call (instantiate) ssl.SSLObject.

New in version 3.8.

Other special directives

These are not used in annotations. They are building blocks for declaring types.

class typing.NamedTuple

Typed version of collections.namedtuple().

Usage:

class Employee(NamedTuple):
    name: str
    id: int

This is equivalent to:

Employee = collections.namedtuple('Employee', ['name', 'id'])

To give a field a default value, you can assign to it in the class body:

class Employee(NamedTuple):
    name: str
    id: int = 3

employee = Employee('Guido')
assert employee.id == 3

Fields with a default value must come after any fields without a default.

The resulting class has an extra attribute __annotations__ giving a dict that maps the field names to the field types. (The field names are in the _fields attribute and the default values are in the _field_defaults attribute, both of which are part of the namedtuple() API.)

NamedTuple subclasses can also have docstrings and methods:

class Employee(NamedTuple):
    """Represents an employee."""
    name: str
    id: int = 3

    def __repr__(self) -> str:
        return f'<Employee {self.name}, id={self.id}>'

Backward-compatible usage:

Employee = NamedTuple('Employee', [('name', str), ('id', int)])

Changed in version 3.6: Added support for PEP 526 variable annotation syntax.

Changed in version 3.6.1: Added support for default values, methods, and docstrings.

Changed in version 3.8: The _field_types and __annotations__ attributes are now regular dictionaries instead of instances of OrderedDict.

Changed in version 3.9: Removed the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.

class typing.NewType(name, tp)

A helper class to indicate a distinct type to a typechecker, see NewType. At runtime it returns an object that returns its argument when called. Usage:

UserId = NewType('UserId', int)
first_user = UserId(1)

New in version 3.5.2.

Changed in version 3.10: NewType is now a class rather than a function.

class typing.TypedDict(dict)

Special construct to add type hints to a dictionary. At runtime it is a plain dict.

TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:

class Point2D(TypedDict):
    x: int
    y: int
    label: str

a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check

assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')

To allow using this feature with older versions of Python that do not support PEP 526, TypedDict supports two additional equivalent syntactic forms:

Point2D = TypedDict('Point2D', x=int, y=int, label=str)
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})

The functional syntax should also be used when any of the keys are not valid identifiers, for example because they are keywords or contain hyphens. Example:

# raises SyntaxError
class Point2D(TypedDict):
    in: int  # 'in' is a keyword
    x-y: int  # name with hyphens

# OK, functional syntax
Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})

By default, all keys must be present in a TypedDict. It is possible to override this by specifying totality. Usage:

class Point2D(TypedDict, total=False):
    x: int
    y: int

This means that a Point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body required.

It is possible for a TypedDict type to inherit from one or more other TypedDict types using the class-based syntax. Usage:

class Point3D(Point2D):
    z: int

Point3D has three items: x, y and z. It is equivalent to this definition:

class Point3D(TypedDict):
    x: int
    y: int
    z: int

A TypedDict cannot inherit from a non-TypedDict class, notably including Generic. For example:

class X(TypedDict):
    x: int

class Y(TypedDict):
    y: int

class Z(object): pass  # A non-TypedDict class

class XY(X, Y): pass  # OK

class XZ(X, Z): pass  # raises TypeError

T = TypeVar('T')
class XT(X, Generic[T]): pass  # raises TypeError

A TypedDict can be introspected via annotations dicts (see Annotations Best Practices for more information on annotations best practices), __total__, __required_keys__, and __optional_keys__.

__total__

Point2D.__total__ gives the value of the total argument. Example:

>>> from typing import TypedDict
>>> class Point2D(TypedDict): pass
>>> Point2D.__total__
True
>>> class Point2D(TypedDict, total=False): pass
>>> Point2D.__total__
False
>>> class Point3D(Point2D): pass
>>> Point3D.__total__
True
__required_keys__

New in version 3.9.

__optional_keys__

Point2D.__required_keys__ and Point2D.__optional_keys__ return frozenset objects containing required and non-required keys, respectively. Currently the only way to declare both required and non-required keys in the same TypedDict is mixed inheritance, declaring a TypedDict with one value for the total argument and then inheriting it from another TypedDict with a different value for total. Usage:

>>> class Point2D(TypedDict, total=False):
...     x: int
...     y: int
...
>>> class Point3D(Point2D):
...     z: int
...
>>> Point3D.__required_keys__ == frozenset({'z'})
True
>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
True

New in version 3.9.

See PEP 589 for more examples and detailed rules of using TypedDict.

New in version 3.8.

Generic concrete collections

Corresponding to built-in types

class typing.Dict(dict, MutableMapping[KT, VT])

A generic version of dict. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Mapping.

This type can be used as follows:

def count_words(text: str) -> Dict[str, int]:
    ...

Deprecated since version 3.9: builtins.dict now supports []. See PEP 585 and Generic Alias Type.

class typing.List(list, MutableSequence[T])

Generic version of list. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Sequence or Iterable.

This type may be used as follows:

T = TypeVar('T', int, float)

def vec2(x: T, y: T) -> List[T]:
    return [x, y]

def keep_positives(vector: Sequence[T]) -> List[T]:
    return [item for item in vector if item > 0]

Deprecated since version 3.9: builtins.list now supports []. See PEP 585 and Generic Alias Type.

class typing.Set(set, MutableSet[T])

A generic version of builtins.set. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as AbstractSet.

Deprecated since version 3.9: builtins.set now supports []. See PEP 585 and Generic Alias Type.

class typing.FrozenSet(frozenset, AbstractSet[T_co])

A generic version of builtins.frozenset.

Deprecated since version 3.9: builtins.frozenset now supports []. See PEP 585 and Generic Alias Type.

Note

Tuple is a special form.

Corresponding to types in collections

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

A generic version of collections.defaultdict.

New in version 3.5.2.

Deprecated since version 3.9: collections.defaultdict now supports []. See PEP 585 and Generic Alias Type.

class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])

A generic version of collections.OrderedDict.

New in version 3.7.2.

Deprecated since version 3.9: collections.OrderedDict now supports []. See PEP 585 and Generic Alias Type.

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])

A generic version of collections.ChainMap.

New in version 3.5.4.

New in version 3.6.1.

Deprecated since version 3.9: collections.ChainMap now supports []. See PEP 585 and Generic Alias Type.

class typing.Counter(collections.Counter, Dict[T, int])

A generic version of collections.Counter.

New in version 3.5.4.

New in version 3.6.1.

Deprecated since version 3.9: collections.Counter now supports []. See PEP 585 and Generic Alias Type.

class typing.Deque(deque, MutableSequence[T])

A generic version of collections.deque.

New in version 3.5.4.

New in version 3.6.1.

Deprecated since version 3.9: collections.deque now supports []. See PEP 585 and Generic Alias Type.

Other concrete types

class typing.IO
class typing.TextIO
class typing.BinaryIO

Generic type IO[AnyStr] and its subclasses TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by open().

Deprecated since version 3.8, will be removed in version 3.12: The typing.io namespace is deprecated and will be removed. These types should be directly imported from typing instead.

class typing.Pattern
class typing.Match

These type aliases correspond to the return types from re.compile() and re.match(). These types (and the corresponding functions) are generic in AnyStr and can be made specific by writing Pattern[str], Pattern[bytes], Match[str], or Match[bytes].

Deprecated since version 3.8, will be removed in version 3.12: The typing.re namespace is deprecated and will be removed. These types should be directly imported from typing instead.

Deprecated since version 3.9: Classes Pattern and Match from re now support []. See PEP 585 and Generic Alias Type.

class typing.Text

Text is an alias for str. It is provided to supply a forward compatible path for Python 2 code: in Python 2, Text is an alias for unicode.

Use Text to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:

def add_unicode_checkmark(text: Text) -> Text:
    return text + u' \u2713'

New in version 3.5.2.

Abstract Base Classes

Corresponding to collections in collections.abc

class typing.AbstractSet(Sized, Collection[T_co])

A generic version of collections.abc.Set.

Deprecated since version 3.9: collections.abc.Set now supports []. See PEP 585 and Generic Alias Type.

class typing.ByteString(Sequence[int])

A generic version of collections.abc.ByteString.

This type represents the types bytes, bytearray, and memoryview of byte sequences.

As a shorthand for this type, bytes can be used to annotate arguments of any of the types mentioned above.

Deprecated since version 3.9: collections.abc.ByteString now supports []. See PEP 585 and Generic Alias Type.

class typing.Collection(Sized, Iterable[T_co], Container[T_co])

A generic version of collections.abc.Collection

New in version 3.6.0.

Deprecated since version 3.9: collections.abc.Collection now supports []. See PEP 585 and Generic Alias Type.

class typing.Container(Generic[T_co])

A generic version of collections.abc.Container.

Deprecated since version 3.9: collections.abc.Container now supports []. See PEP 585 and Generic Alias Type.

class typing.ItemsView(MappingView, Generic[KT_co, VT_co])

A generic version of collections.abc.ItemsView.

Deprecated since version 3.9: collections.abc.ItemsView now supports []. See PEP 585 and Generic Alias Type.

class typing.KeysView(MappingView[KT_co], AbstractSet[KT_co])

A generic version of collections.abc.KeysView.

Deprecated since version 3.9: collections.abc.KeysView now supports []. See PEP 585 and Generic Alias Type.

class typing.Mapping(Sized, Collection[KT], Generic[VT_co])

A generic version of collections.abc.Mapping. This type can be used as follows:

def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
    return word_list[word]

Deprecated since version 3.9: collections.abc.Mapping now supports []. See PEP 585 and Generic Alias Type.

class typing.MappingView(Sized, Iterable[T_co])

A generic version of collections.abc.MappingView.

Deprecated since version 3.9: collections.abc.MappingView now supports []. See PEP 585 and Generic Alias Type.

class typing.MutableMapping(Mapping[KT, VT])

A generic version of collections.abc.MutableMapping.

Deprecated since version 3.9: collections.abc.MutableMapping now supports []. See PEP 585 and Generic Alias Type.

class typing.MutableSequence(Sequence[T])

A generic version of collections.abc.MutableSequence.

Deprecated since version 3.9: collections.abc.MutableSequence now supports []. See PEP 585 and Generic Alias Type.

class typing.MutableSet(AbstractSet[T])

A generic version of collections.abc.MutableSet.

Deprecated since version 3.9: collections.abc.MutableSet now supports []. See PEP 585 and Generic Alias Type.

class typing.Sequence(Reversible[T_co], Collection[T_co])

A generic version of collections.abc.Sequence.

Deprecated since version 3.9: collections.abc.Sequence now supports []. See PEP 585 and Generic Alias Type.

class typing.ValuesView(MappingView[VT_co])

A generic version of collections.abc.ValuesView.

Deprecated since version 3.9: collections.abc.ValuesView now supports []. See PEP 585 and Generic Alias Type.

Corresponding to other types in collections.abc

class typing.Iterable(Generic[T_co])

A generic version of collections.abc.Iterable.

Deprecated since version 3.9: collections.abc.Iterable now supports []. See PEP 585 and Generic Alias Type.

class typing.Iterator(Iterable[T_co])

A generic version of collections.abc.Iterator.

Deprecated since version 3.9: collections.abc.Iterator now supports []. See PEP 585 and Generic Alias Type.

class typing.Generator(Iterator[T_co], Generic[T_co, T_contra, V_co])

A generator can be annotated by the generic type Generator[YieldType, SendType, ReturnType]. For example:

def echo_round() -> Generator[int, float, str]:
    sent = yield 0
    while sent >= 0:
        sent = yield round(sent)
    return 'Done'

Note that unlike many other generics in the typing module, the SendType of Generator behaves contravariantly, not covariantly or invariantly.

If your generator will only yield values, set the SendType and ReturnType to None:

def infinite_stream(start: int) -> Generator[int, None, None]:
    while True:
        yield start
        start += 1

Alternatively, annotate your generator as having a return type of either Iterable[YieldType] or Iterator[YieldType]:

def infinite_stream(start: int) -> Iterator[int]:
    while True:
        yield start
        start += 1

Deprecated since version 3.9: collections.abc.Generator now supports []. See PEP 585 and Generic Alias Type.

class typing.Hashable

An alias to collections.abc.Hashable.

class typing.Reversible(Iterable[T_co])

A generic version of collections.abc.Reversible.

Deprecated since version 3.9: collections.abc.Reversible now supports []. See PEP 585 and Generic Alias Type.

class typing.Sized

An alias to collections.abc.Sized.

Asynchronous programming

class typing.Coroutine(Awaitable[V_co], Generic[T_co, T_contra, V_co])

A generic version of collections.abc.Coroutine. The variance and order of type variables correspond to those of Generator, for example:

from collections.abc import Coroutine
c: Coroutine[list[str], str, int]  # Some coroutine defined elsewhere
x = c.send('hi')                   # Inferred type of 'x' is list[str]
async def bar() -> None:
    y = await c                    # Inferred type of 'y' is int

New in version 3.5.3.

Deprecated since version 3.9: collections.abc.Coroutine now supports []. See PEP 585 and Generic Alias Type.

class typing.AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])

An async generator can be annotated by the generic type AsyncGenerator[YieldType, SendType]. For example:

async def echo_round() -> AsyncGenerator[int, float]:
    sent = yield 0
    while sent >= 0.0:
        rounded = await round(sent)
        sent = yield rounded

Unlike normal generators, async generators cannot return a value, so there is no ReturnType type parameter. As with Generator, the SendType behaves contravariantly.

If your generator will only yield values, set the SendType to None:

async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
    while True:
        yield start
        start = await increment(start)

Alternatively, annotate your generator as having a return type of either AsyncIterable[YieldType] or AsyncIterator[YieldType]:

async def infinite_stream(start: int) -> AsyncIterator[int]:
    while True:
        yield start
        start = await increment(start)

New in version 3.6.1.

Deprecated since version 3.9: collections.abc.AsyncGenerator now supports []. See PEP 585 and Generic Alias Type.

class typing.AsyncIterable(Generic[T_co])

A generic version of collections.abc.AsyncIterable.

New in version 3.5.2.

Deprecated since version 3.9: collections.abc.AsyncIterable now supports []. See PEP 585 and Generic Alias Type.

class typing.AsyncIterator(AsyncIterable[T_co])

A generic version of collections.abc.AsyncIterator.

New in version 3.5.2.

Deprecated since version 3.9: collections.abc.AsyncIterator now supports []. See PEP 585 and Generic Alias Type.

class typing.Awaitable(Generic[T_co])

A generic version of collections.abc.Awaitable.

New in version 3.5.2.

Deprecated since version 3.9: collections.abc.Awaitable now supports []. See PEP 585 and Generic Alias Type.

Context manager types

class typing.ContextManager(Generic[T_co])

A generic version of contextlib.AbstractContextManager.

New in version 3.5.4.

New in version 3.6.0.

Deprecated since version 3.9: contextlib.AbstractContextManager now supports []. See PEP 585 and Generic Alias Type.

class typing.AsyncContextManager(Generic[T_co])

A generic version of contextlib.AbstractAsyncContextManager.

New in version 3.5.4.

New in version 3.6.2.

Deprecated since version 3.9: contextlib.AbstractAsyncContextManager now supports []. See PEP 585 and Generic Alias Type.

Protocols

These protocols are decorated with runtime_checkable().

class typing.SupportsAbs

An ABC with one abstract method __abs__ that is covariant in its return type.

class typing.SupportsBytes

An ABC with one abstract method __bytes__.

class typing.SupportsComplex

An ABC with one abstract method __complex__.

class typing.SupportsFloat

An ABC with one abstract method __float__.

class typing.SupportsIndex

An ABC with one abstract method __index__.

New in version 3.8.

class typing.SupportsInt

An ABC with one abstract method __int__.

class typing.SupportsRound

An ABC with one abstract method __round__ that is covariant in its return type.

Functions and decorators

typing.cast(typ, val)

Cast a value to a type.

This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).

@typing.overload

The @overload decorator allows describing functions and methods that support multiple different combinations of argument types. A series of @overload-decorated definitions must be followed by exactly one non-@overload-decorated definition (for the same function/method). The @overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a @overload-decorated function directly will raise NotImplementedError. An example of overload that gives a more precise type than can be expressed using a union or a type variable:

@overload
def process(response: None) -> None:
    ...
@overload
def process(response: int) -> tuple[int, str]:
    ...
@overload
def process(response: bytes) -> str:
    ...
def process(response):
    <actual implementation>

See PEP 484 for details and comparison with other typing semantics.

@typing.final

A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example:

class Base:
    @final
    def done(self) -> None:
        ...
class Sub(Base):
    def done(self) -> None:  # Error reported by type checker
        ...

@final
class Leaf:
    ...
class Other(Leaf):  # Error reported by type checker
    ...

There is no runtime checking of these properties. See PEP 591 for more details.

New in version 3.8.

@typing.no_type_check

Decorator to indicate that annotations are not type hints.

This works as class or function decorator. With a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses).

This mutates the function(s) in place.

@typing.no_type_check_decorator

Decorator to give another decorator the no_type_check() effect.

This wraps the decorator with something that wraps the decorated function in no_type_check().

@typing.type_check_only

Decorator to mark a class or function to be unavailable at runtime.

This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:

@type_check_only
class Response:  # private or not available at runtime
    code: int
    def get_header(self, name: str) -> str: ...

def fetch_response() -> Response: ...

Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.

Introspection helpers

typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False)

Return a dictionary containing type hints for a function, method, module or class object.

This is often the same as obj.__annotations__. In addition, forward references encoded as string literals are handled by evaluating them in globals and locals namespaces. If necessary, Optional[t] is added for function and method annotations if a default value equal to None is set. For a class C, return a dictionary constructed by merging all the __annotations__ along C.__mro__ in reverse order.

The function recursively replaces all Annotated[T, ...] with T, unless include_extras is set to True (see Annotated for more information). For example:

class Student(NamedTuple):
    name: Annotated[str, 'some marker']

get_type_hints(Student) == {'name': str}
get_type_hints(Student, include_extras=False) == {'name': str}
get_type_hints(Student, include_extras=True) == {
    'name': Annotated[str, 'some marker']
}

Note

get_type_hints() does not work with imported type aliases that include forward references. Enabling postponed evaluation of annotations (PEP 563) may remove the need for most forward references.

Changed in version 3.9: Added include_extras parameter as part of PEP 593.

typing.get_args(tp)
typing.get_origin(tp)

Provide basic introspection for generic types and special typing forms.

For a typing object of the form X[Y, Z, ...] these functions return X and (Y, Z, ...). If X is a generic alias for a builtin or collections class, it gets normalized to the original class. If X is a union or Literal contained in another generic type, the order of (Y, Z, ...) may be different from the order of the original arguments [Y, Z, ...] due to type caching. For unsupported objects return None and () correspondingly. Examples:

assert get_origin(Dict[str, int]) is dict
assert get_args(Dict[int, str]) == (int, str)

assert get_origin(Union[int, str]) is Union
assert get_args(Union[int, str]) == (int, str)

New in version 3.8.

typing.is_typeddict(tp)

Check if a type is a TypedDict.

For example:

class Film(TypedDict):
    title: str
    year: int

is_typeddict(Film)  # => True
is_typeddict(list | str)  # => False

New in version 3.10.

class typing.ForwardRef

A class used for internal typing representation of string forward references. For example, List["SomeClass"] is implicitly transformed into List[ForwardRef("SomeClass")]. This class should not be instantiated by a user, but may be used by introspection tools.

Note

PEP 585 generic types such as list["SomeClass"] will not be implicitly transformed into list[ForwardRef("SomeClass")] and thus will not automatically resolve to list[SomeClass].

New in version 3.7.4.

Constant

typing.TYPE_CHECKING

A special constant that is assumed to be True by 3rd party static type checkers. It is False at runtime. Usage:

if TYPE_CHECKING:
    import expensive_mod

def fun(arg: 'expensive_mod.SomeType') -> None:
    local_var: expensive_mod.AnotherType = other_fun()

The first type annotation must be enclosed in quotes, making it a “forward reference”, to hide the expensive_mod reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.

Note

If from __future__ import annotations is used, annotations are not evaluated at function definition time. Instead, they are stored as strings in __annotations__. This makes it unnecessary to use quotes around the annotation (see PEP 563).

New in version 3.5.2.